University Students’ Perspectives on Autonomous Vehicle Adoption: Adelaide Case Study

Abstract

Self-driving cars are getting closer to becoming a reality, with some potential benefits including increased road safety and improved traffic flow. Most research to date have focused on the general public as potential users of autonomous vehicles (AVs), whereas this study surveyed 165 students from the University of South Australia, Adelaide, on their perspectives, particularly on travel patterns and factors influencing AV acceptance. The study made use of structural equation modelling (SEM) in R programming environment. The findings revealed that applied science students and those who drive to campus are more likely to use AV technology than other groups. The younger male students favoured AV selection more than the female students. The study, on the other hand, found that the majority of students are concerned about cyber-security and the failure of AVs, but are willing to adopt AV technology in the case of a no-cost barrier. This research would be useful in determining what infrastructures, policies, and strategies are required to prepare for the introduction of AV technology in closed environments such as university campuses.

Full text

University students’ perspectives on autonomous vehicle adoption:
Adelaide case study
Ali Soltani
a, b,*
, Dhawala Ananda
a
, Monorom Rith
c, d
a UniSA Creative, UniSA, Adelaide, Australia
b
Shiraz University, Shiraz, Iran
c School of Civil Engineering and Technology, Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani 12120, Thailand
d The Joint Graduate School of Energy and Environment, King Mongkut’s University of Technology Thonburi, Bangkok 10140, Thailand
Ali Soltani, Dhawala Ananda, Monorom Rith,
University students’ perspectives on autonomous vehicle adoption: Adelaide case study,
Case Studies on Transport Policy,
Volume 9, Issue 4,
2021,
Pages 1956-1964,
ISSN 2213-624X, https://doi.org/10.1016/j.cstp.2021.11.004.
A R T I C L E I N F O
Keywords:
Autonomous vehicle (AV)
Personal attitudes
Campus commuting
Structural equation modelling (SEM)
Australia
A B S T R A C T
Self-driving cars are getting closer to becoming a reality, with some potential benefits including increased road
safety and improved traffic flow. Most research to date have focused on the general public as potential users of
autonomous vehicles (AVs), whereas this study surveyed 152 students from the University of South Australia,
Adelaide, on their perspectives, particularly on travel patterns and factors influencing AV acceptance. The study
made use of structural equation modelling (SEM) in R programming environment. The findings revealed that
applied science students and those who drive to campus are more likely to use AV technology than other groups.
The younger male students favoured AV selection more than the female students. The study, on the other hand,
found that the majority of students are concerned about cyber-security and the failure of AVs, but are willing to
adopt AV technology in the case of a no-cost barrier. This research would be useful in determining what in-
frastructures, policies, and strategies are required to prepare for the introduction of AV technology in closed
environments such as university campuses.
1.
Introduction
With a growing rate of students enrolled in universities, there has
been a gradual increase in the number of universities in urban areas. In
recent decades, the travel patterns of university students have become
an intriguing research topic (Bonham and Koth, 2010, Rissel et al.,
2013). Several factors have been identified as significant predictors of
different travel patterns and behaviours among the student population
(Danaf et al., 2014, Nash and Mitra, 2019). In order to quickly adapt to a
new environment, university students’ travel patterns and preferences in
their place of study tend to change (Bae and Song, 2017, Field, 1999).
Students’ educational trips to Australia’s capital cities, such as Sydney,
Melbourne, and Adelaide, have proven to be the most popular. Since
2016, the percentage of people travelling to the city of Adelaide for
education has increased from 13% to 24% (Adelaide City Council 2017).
Tertiary educational trips have increased massively and contributed
significantly to urban traffic, owing to their dispersed origins and flex-
ible scheduling of student trips across the metropolitan area (Soltani
et al., 2019a,b).
The pattern of trips made to institutions differs from that of the
general population, with students using different modes of trans-
portation to get to their university every day (Khattak et al., 2011).
University students, with their sporadic lesson plans and significant
freedom on campus, are an excellent example of a social group that has a
complex and varied commuting pattern (Limanond et al., 2011, Zhan et
al., 2016). The arrival of autonomous vehicles (AVs) is not far away, and
transportation planners and policymakers should consider the
necessary policy frameworks for a smooth introduction of this cutting-
edge technology. Intellectual planning based on a clear vision and
consistent actions will aid in addressing the various AV challenges that
are intertwined with students’ commuting patterns (Fagnant and
Kockelman, 2015).
The disruptive effects of advanced technology have recently emerged
as a hot topic of debate and concern among urban planners and related
engineers (Rouse et al., 2018). More research and study are needed to
lay the groundwork for policy development in this context (Guerra, 2016,
Hawkins and Nurul Habib, 2019). Many academic sources shed light on
AVs by connecting urban environments and land use to travel
behaviour. Policy suggestions and recommendations for land use
changes have been investigated in the last decade (Maurer et al., 2016),

but policies for AV adoption related to students’ travel patterns have
received less attention.
The need for AV policy emerges as a recurring theme among aca-
demics, and applying current methods used for conventional vehicles to
AVs would have negative consequences in the current urban setting
(Kane and Whitehead, 2017). In addition, in recent years, scholars have
focused on conducting studies to understand the impacts of AVs in urban
landscape planning perspectives, such as traffic safety (Fagnant and
Kockelman, 2015, Winkle, 2016), accessibility and social inclusion
(Smith and Anderson, 2017, Winkle, 2016), parking footprint (Nour-
inejad et al., 2018), and congestions and greenhouse gas (GHG) emis-
sions (Zhou et al., 2017). It is very likely that AVs will be introduced in
closed settings such as university campuses, airports, vacation parks,
and retirement villages (Miralles-Guasch and Domene, 2010); Kaur and
Rampersad, 2018). Notably, there is very little observed/quantitative
evidence to support the benefits and drawbacks of AVs in university
campuses.
The primary objective of this study is to determine the effects of
sociodemographic characteristics, travel behaviour, and attitudes to-
wards AV technology on AV knowledge, interesting AV type, and AV
choice among Australian college students. This objective is met by
conducting a survey of attitudes among tertiary students at the Uni-
versity of South Australia (UniSA), in Adelaide, one of Australia’s most
car-dependent cities (Nguyen et al., 2018). As international students
make up a large proportion of Australian universities, they frequently
exhibit distinct travel and activity behaviours and patterns as a result of
their socioeconomic background (Rissel et al., 2013, Soltani et al.,
2019a).
The primary dataset of 152 observations collected in 2019 was used
in the study. For analysing the data, two consequential statistical
methods are used: a) confirmatory factor analysis (CFA) is used to
extract factors of the attitudinal factors towards AV technology, and b)
structural equation modelling (SEM) is used to understand the impacts
of the input variables on the output variables. This project adds to
previous research by taking into account the attitudes and willingness of
college students, one of the most interested social groups in innovative
transportation measures. To the best of our knowledge, this is one of the
earliest studies in Australia to use advanced statistical techniques to
correlate university students’ opinions on AVs with their travel patterns.
2.
Background studies
AVs are the most innovative model of the smart transportation sys-
tem. It is a current hot topic of discussion in the era of sustainable urban
mobility and smart city development (Faisal et al., 2019). AVs are based
on the basic idea of moving vehicles electronically and/or mechanically
using devices that replace some or all of the work done by humans
(Shladover, 2018). An abundance of recent scholarly works on the for-
mation of AVs is available, focusing on a wide range of domains, pri-
marily related to the advancement of engineering and information
technology (IT) fields in AVs such as automobile design and cyber-
security (Chasel, 2017). While, there is little academic consensus on
the most likely types of AVs, three main possible forms of AV use and
operation are presented: 1) privately owned AVs by households, 2)
privately owned group of AVs operated by organisations (e.g., ‘Uber,’
‘Lyft,’ and ‘car2go,’ and 3) publicly owned group of AVs operated by the
government (e.g., ‘Public transportation’). Each of these types of AVs is
expected to have different effects on short-range mobility patterns and
long-term urban structure (Faisal et al., 2019, Finn, 2018, Hawkins and
Nurul Habib, 2019, Zmud and Sener, 2017). According to a study con-
ducted at ETH Zurich, privately owned AVs may not be popular in the
future due to the sophisticated environment of 24/7 mobility-on-
demand
(
H
o
¨
rl
et
al., 2016
).
Shared AV (SAV) is considered as part of broad technology of AVs,
and is designed for sharing between multiple users.The deployment of
SAV is the most likely to gain public interest because these services are
offered on-demand, allowing passengers to share the same journey with
only a minor increase in travel time and costs. Mobility on demand is
well suited to the larger and evolving field of transportation and shared
economy. Public AVs offer more advantages, such as consistency, uni-
form standards, and easy integration with existing infrastructure. Gov-
ernment agencies are eager to adopt advanced on-demand service
technology by collaborating with ridesharing service providers to
improve multimodal transportation system integration (Geron, 2017,
Lamotte et al., 2016). The SAV concept supports the idea of reducing the
number of parking spaces in urban areas while also providing insight
into how future land use planning can be developed to support sus-
tainable mobility (Okeke, 2020). Another recent study found that
combining AV with a new mobility service could reduce reliance on
private cars, and that the automobile industry, car-sharing operators,
and policymakers should chart a path to benefit society (Galich and
Stark, 2021).
Three primary research shifts are proposed in the areas of societal
acceptance (from consumer adoption to citizen acceptance), societal
implications (from short-term to long-term implications), and AV
governance (Milakis and Müller, 2021). AV knowledge is defined as “the
level of knowledge a human being has about AVs.” Prior knowledge of
technology can be defined as evaluating that technology prior to inter-
acting with it (Davis, 1989). In other words, it is the knowledge that
individuals have gained about AVs, which they then analyse in order to
comprehend and evaluate the effects of the same on their travel patterns
and lifestyle. Prior knowledge will provide insights into the potential
issues that a student may face when using an AV (Payre et al., 2014).
The university setting is one of the most appropriate places to
broaden students’ knowledge of AVs, indicating that students are highly
prone to having sound information and can easily choose whether or not
to use an AV. Highly educated people prefer SAVs and personal AVs
equally, and they may be more aware of the concept of AVs and more
willing to accept new ideas and developments (Haboucha et al., 2017).
The effects of students’ perceptions and behaviours towards driverless
vehicles on their travel activities have piqued the interest of researchers.
Furthermore, a some background studies indicate that more informed
and well-educated people are generally in favour of using AVs (Bansal
et al., 2016, Hudson et al., 2019).
The socioeconomic characteristic such as income, age, gender, level
of education, and family size (Becker and Axhausen, 2017, Cartenì,
2020) in addition to travel patterns consisting of travel distance, travel
time, departure time, and trip frequency (Danaf et al., 2014, Payre et al.,
2014) influence the decision of using or not using an AV. In this regard,
travellers who use intermodal transport are more likely to choose AV
(Krueger et al., 2016). Furthermore, trip route (based on congestion
level) and waiting time are among the factors influencing AV selection
(Bansal et al., 2016). Other factors, such as work status, the location of
residence or job, and the availability of alternative vehicles, can also
explain AV selection (Giuliano, 2003, Klein et al., 2018). According to
the simulation results, AV penetration in road traffic lowers total travel
cost and time while slightly increasing travel distance (Madadi et al.,
2019).
Preference for AVs is closely related to current attitudes towards
existing motorised modes of transportation (Acheampong et al., 2021).
Furthermore, social attitudes related to technological innovation and
urban politics drive the adoption of self-driving cars (Cugurullo et al.,
2020). Personal attitudes towards AVs are one of the most important
factors influencing willingness to use AVs. As with any new technology,
AVs will generate a variety of risks and negative effects, reducing their
public acceptance (Taeihagh and Lim, 2019). Public trust is seen as one
of the main obstacles to Avs adoption (Kaur and Rampersad, 2018).
Many people are concerned about the safety and security of AVs, making
them hesitant to use them. On the other hand, the more comfortable
people are with the idea of using AV, the more likely they are to accept it
(Xu et al., 2018). Given the level of connectivity and advanced AV
technology, developers and service-providers must protect their

networks from cyber attacks (Morando et al., 2018). Furthermore, in
order for the AV cyber risk rating system to be effective, it must be able
to adapt to the changing nature of risks (Alawadhi et al., 2020). There is
always the risk of service providers and AV suppliers misusing AV users’
private information (Collingwood, 2017).
According to the preceding argument, the three influential groups of
factors have an impact on various endogenous variables related to AVs,
as shown in Fig. 1. Socioeconomic characteristics, commuting patterns,
and attitudes towards AVs are the three groups. The intention to use
AVs, knowledge of AVs, and interesting types of AV are three endoge-
nous variables related to AVs. Each group contains variables that have
an impact on these endogenous variables. Gender, age, student
employment status, and fortnightly income are among socioeconomic
characteristics. The commuting pattern group specifies the student’s
mode of transportation, frequency of travel, and distance travelled to
and from the campus from his or her residence. The attitudes section
contains a variety of statements about how AVs work, which in turne, it
provides a deeper understanding of students’ perspectives on the
acceptance and the potential use of AV. The knowledge of AV technol-
ogy may also influence attitudes towards AVs (Pettigrew et al., 2019)
because attitudes are usually shaped by information as an endogenous
factor.
Based on the literature, the following 10 items are defined and used
to assess students’ attitudes towards AVs and their various features:
Item1. Self-driving vehicles will offer us new technical possibilities,
alternatives and products (Guo et al., 2019, Fagnant and Kockelman,
2015).
Item2. Self-driving vehicles can reach destinations safely (Xu et al.,
2018, Bagloee et al., 2016).
Item3. Self-driving vehicles will have legal issues (e.g., public pol-
icies, traffic code, and technical standards) (Rosique et al., 2019,
Taeihagh and Lim, 2019, Ryan, 2020).
Item4. Self-driving vehicles will have moral and ethical issues (e.g.,
judging extreme situations) (Kallioinen et al., 2019, Coeckelbergh,
2016, Taeihagh and Lim, 2019).
Item5. Self-driving vehicles may lessen the traffic congestion and
delays, hence travel time is reduced (Fagnant and Kockelman, 2015,
Figliozzi, 2020).
Item6. Self-driving vehicles may lower air pollution and greenhouse
gases with better fuel efficiency (Wadud et al., 2016, Massar et al.,
2021, Greenblatt and Saxena, 2015).
Item7. Public parking costs may be reduced (Fagnant and Kockel-
man, 2015, Heinrichs and Cyganski, 2015).
Item8. Computer malfunctions may lead to accidents (some unex-
pected glitch that causes the machine to act unpredictably or stop
altogether) (Wang et al., 2020, Mordue et al., 2020).
Item9. AV will increase accessibility (for those with disabilities and
without a driving license) (Dianin et al., 2021, Lee and Mirman,
2018).
Item10. AV is vulnerable to remote control and hacking: cyber-
security (privacy of an individual may be affected) (Morando et al.,
2018, Alawadhi et al., 2020, Collingwood, 2017).
3.
Data sources and descriptive statistics
The survey data was collected over a two month period in October
and November 2019 at three different locations, namely: the Mawson
Lakes, City East, and Magill as the major campuses of the University of
South Australia (UniSA), Adelaide (Fig. 2). UniSA, with a student pop-
ulation of approximately 32,000, is a public university and the largest in
the state of South Australia. The UniSA human research ethics com-
mittee (reference # 202584) granted ethics approval for this study, and
all participants provided informed consent. The contributions of par-
ticipants through the Google Form as a web-based instrument were
guaranteed to be anonymous. The planned questions concern socio-
economic characteristics, travel behaviour, attitudes towards AV adop-
tion, and endogeneous variables related to AV adoption. One of those
questions, with ten items, was designed to measure the attitudes towards
AV as described in the previous section.
Following the distribution of the survey flyer on the three campuses,
165 students volunteered to participate, and submittd their responses.
For statistical analysis, only 152 observations with completed responses
were used. Although there is no widely accepted rule for determining
sample size in SEM models, it is determined by the model’s complexity
and the distributional characteristics of the observed variables (Kline,
2015, Hadiuzzman et al., 2017). A proportion of five observations for
each variable is sufficient in a data set with a normal distribution,
assuming that the latent variable has multiple indicators (Bentler and
Chou, 1987). A sample size of 150 is sufficient for a SEM model with a
Fig. 1. A conceptual framework of the acceptance and adoption of AVs. Fig. 2. Location of UniSA campuses (basemap: Google.com).
•
•
•
•
•
•
•
•
•
•

normally distributed indicator variable and no missing data, according
to
a
Monte
Carlo
simulation
(
Muth
´
en
and
Muth
´
en,
2002
).
Table 1 displays the descriptive statistics for socioeconomic charac-
teristics, travel behaviour, and AV-related variables. The majority of
students (36.84%) were between the ages of 25 and 27, and the majority
of respondents (73.03%) were postgraduate students. Males (51.97
percent) and international students made up the majority of respondents
(54.61 percent). In proportions of 33.55 percent, 35.53 percent, and
30.92 percent, students studied applied science, social science, and
health science, respectively. One fifth of students earn less than $500 per
month, one third earn $500 to $1000 per month, and the remainder earn
more than $1000 per month.
It was also discovered that the vast majority of students (roughly
three-quarters of the dataset) drove to campus two or three days per
week (38.16 percent). Most of them live within a two-kilometer radius of
campus (37.09 percent). The preponderance of respondents (45.39
percent) have a moderate understanding of AV technology, are
Table 1
Descriptive statistics of the socioeconomic characteristics, travel behaviour, and
AV adoption.
Variable Percentage
Gender
Male 51.97%
Female 48.03%
Age
18
–
21 years 13.16%
22
–
24 years 21.05%
25
–
27 years 36.84%
28
–
30 years 16.45%
≥
30 years 12.50%
Student type
Domestic Student 45.39%
International Student 54.61%
Student status
Undergraduate 26.97%
Postgraduate (i.e., Master
’
s and Ph.D. student) 73.03%
Field of study
Applied science (e.g., IT and Engineering)
33.55%
Social science (e.g., education and arts)
35.53%
Health science (e.g., life science)
30.92%
Monthly income
≤
$500
20.39%
$501
–
$1000
34.87%
>
$1000
44.74%
Distance from home to the campus
≤
2 km
37.09%
>
2
–
4 km
27.15%
>
4
–
6 km
24.50%
>
6 km
11.26%
Days of travel to the campus per week
1 day
5.26%
2 days
37.50%
3 days
36.18%
4 days
19.08%
5 days
1.97%
Current travel mode choice to the campus
Walk
14.47%
Cycle
&
Two-wheeler
12.50%
Car as a driver
38.16%
Car as a passenger
6.58%
Public transport
28.29%
Knowledge of AV technology
Weak
15.13%
Medium
45.39%
Strong
39.47%
AV type that interests respondents
Private car 23.03%
Non-private car (e.g., shared car, public transport, taxi, and cab)
76.97%
AV choice in the case of no budget barrier
No 44.08%
Yes 55.92%
Total
=
152 counts
interested in non-private AV (76.97 percent), and prefer AV if funding is
not an issue (55.92 percent). The AV technology knowledge is self-
reported, and it refers to respondents’ familiarity with AV technology.
In most cases, travel time is significantly associated with travel dis-
tance. Where real driving time is lacking in commuting study, Rietveld
et al. (1999) discovered that network trip distance may be utilised as an
effective proxy. Using trip origin and destination points stated by re-
spondents, we computed the objective travel distance by each individual
mode. After geocoding the postal codes of all sampled students’ resi-
dence (origin) and campus (destination), ESRI ArcGIS 10.2.2 was used to
calculate the distance from home to the campus. Because travel time
estimations are more susceptible to the computation methods utilised,
this method can provide more precision (Salonen and Toivonen, 2013).
Waiting time is a significant performance parameter for quantifying the
quality of public transport services (Liao et al., 2020), despite the fact
that in our study, the bulk of commuting was done in private cars, which
had little waiting time.
The attitudes towards AVs consist of 10 items as below (Fig. 3). All of
the items were evaluated using a five-point Likert scale, with scores
ranging from 1 (very unlikely) to 5 (very likely) (very likely).
4.
Methodology
There are two steps of data analysis in the study: 1) exploratory
factor analysis (EFA) for attitudes towards AVs and 2) structural equa-
tion modelling (SEM). EFA is a data-driven approach for investigating
the relationship among variables and determining the smallest number
of new variables that can effectively describe multidimensional data
(Ockey, 2013). In our study, we used the EFA to account for the variables
of attitudes towards AVs and create latent variables for SEM. SEM is an
integrated multivariate technique that uses the hypothesis-testing
approach to analyse a structural theory bearing on some phenomenon
(Hoyle, 2012). SEM can have at least two dependent variables, each of
which affects other dependent variables in a complex system. Further-
more, it can capture the direct and indirect effects of the explanatory
variables on other variables and allow variables to be correlated.
4.1.
Exploratory factor analysis (EFA)
EFA is applied to address the multicollinearity problem among
observed variables and reduce the complexity of the regression analysis
(Ockey, 2013). To put it another way, EFA is used to transform a set of
Fig. 3.
Scoring attitudes towards AV.

A. Soltani et al.
1960
correlated observed variables into a set of uncorrelated unobservable
variables. In our study, we used the principal component method in
conjunction with the varimax rotation approach for EFA. Before per-
forming PCA, it is important to ensure that the survey data fulfils the
PCA assumptions in order to get accurate findings (Hair et al., 2009).
Based on the Likert 5-point scale, our data is in order, and our variables
fulfil the criteria for linear correlation. To find correlation, the Bartlett
’
s
roundness test is employed. The Kaiser Meyer Olkin (KMO) of the entire
data set was computed to assess sample size sufficiency. The advantage
of Varimax rotation is that it can maximize the variance in the load within
the factor, while maximizing the difference between the high and low
loads in a specific factor (The Statistical Consulting Center of the UCLA,
2021).
In addition, the values of the bivariate correlation matrix of all items
should be checked to ensure that they are highly correlated with each
other. There are ten variables (items): x
1
,
x
2
,
⋯
,
x
10
. A new variable
(principal component) that is a combination of the ten variables is
expressed as: y
j
=
a
j1
x
1
+
a
j2
x
2
+
⋯
+
a
j10
x
10
, where a
ji
(i
=
1, 2,
…
,
10) is a parameter estimate that corresponds to xi for a new variable yj.
This linear model represents the correlations that exist between the
observed variables and the extracted factors or the unobserved con-
structs. (Javid et al., 2021). A principal component is a linear combi-
nation that maximises a possible variance, and a principal component is
orthogonal to the other principal components.
The software “SPSS Statistics 20” was used for EFA of the attitudes
towards AVs in our study. A dataset with more than 100 observations is
quite reliable for EFA (Kline, 2015). Only the components with eigen-
values greater than one can be considered as the principal components
(Kaiser, 1960), and only the factor loadings higher than 0.5 are reported
(Choocharukul et al., 2006). Guidance for determining a cutoff of a factor
loading is a little hazy (Ockey, 2013). Four components with ei-
genvalues higher than one and items with factor loadings higher than
0.5 in absolute value were deliberately extracted for our study, see
Table 2. The four selected principal components can account for 59.73%
of the total variance. The table lists the items from the highest loading to
the lowest. The rotated factor loadings define the magnitude of influence
of each item on the corresponding principal component. Components 1,
2, 3, and 4 were labeled Attitude 1, Attitude 2, Attitude 3, and Attitude 4,
respectively. Attitude 1 consists of items 4, 10, and 7. Attitude 2 is made
up of two items: items 5 and 6. Attitude 3 comprises items 2, 1, and
9. The last attitude consists of items 8 and 3. We name them as below:
Attitude 1: Ethical, parking, and hacking concerns [Security
concerns]
Attitude 2: Reducing congestion and pollution [Environmental
concerns]
Attitude 3: Technology, safety and accessibility opportunities
Table 2
Principal components of attitudes towards AVs.
[Mobility concerns]
Attitude 4: Legal concern and accident [Safety concerns].
To control the complexity of the SEM, we included the four devel-
oped components for hypothesis testing.
4.2.
Structural equation modeling (SEM)
The SEM is designed to determine the socioeconomic characteristics,
travel behaviour, and attitudinal factors influence three output variables
relevant to AV (i.e., knowledge level of AV technology, interesting AV
type, and AV choice), as well as how these three output variables are
statistically related (Fig. 4). The knowledge of AV technology is ar-
ranged as the ordinal variable, while the other output variables are ar-
ranged as the dichotomous nominal variables. The input variables that
are arranged for SEM are listed in Table 3. The age factor is classified as
an ordinal variable because the intervals of each category are equiva-
lent, and it can reduce the number of dummy variables. The same
concept and logic are applied to the factors of monthly income and days
of travel. The other input variables are arranged as the nominal vari-
ables, other than the distance factor that is considered as the continuous
variable.
The package “lavaan” of R programming language was used for
statistical data analysis because this package is open-source and can
estimate a multivariate statistical model with categorical output vari-
ables (i.e., ordinal variable and dichotomous nominal variable) (Rosseel,
2020). The features of the package therefore can complete the study
objectives. The diagonally weighted least squares (DWLS) estimator was
used to estimate the model parameters because the DWLS estimator can
compute robust standard errors and a mean- and variance-adjusted test
statistic (Rosseel, 2020). We used the backward elimination approach to
remove the insignificant input variables at the 0.1 significance level. The
goodness-of-fit of the developed structure model is assessed based on
some criteria. The cutoff of the comparative fit index (CFI) (Bentler,
1990), the Tucker-Lewis index (TLI) (Tucker and Lewis, 1973) should be
>0.95, and the root mean square error of approximation (RMSEA)
should be less than 0.06 (Steiger, 1980).
5.
Model estimation results
The streamlined model is created after 28 iterations. The CFI, TLI,
and RMSEA of the streamlined model are 0.91, 0.97, and 0.05, respec-
tively. The CFI is close to the cut-off criterion of the model goodness of
fit, whereas the TLI and RMSEA are within a range of the cut-off criteria.
Correspondingly, the streamlined model is a good fit for the sample data
(Table 4).
Surprisingly, monthly income and distance from home to the
Item
Component
Attitude 1
Attitude 2
Attitude 3
Attitude 4
(Security
(Environmental
(Mobility
(Safety
concerns)
concerns)
concerns)
concerns)
Item4
0.739
Item10
0.650
Item7
0.620
Item5
0.740
Item6
0.738
Item2
0.758
Item1
0.561
Item9
Item8
—
0.502
0.701
Item3
0.549
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
Rotation converged in 22 iterations.
Fig. 4.
A model structure of the study.

A. Soltani et al.
Case Studies on Transport Policy 9 (2021) 1956–1964
1961
Table 3
The arrangement of input variables for statistical data analysis.
Table 4
Model estimation results
–
coefficients (standard error).
Gender Nominal Male
=
1; Female
=
0 (ref.)
Age Ordinal 18
–
21 years
=
1;
technology
Observed variable
22
–
24 years
=
2;
25
–
27 years
=
3;
28
–
30 years
=
4;
≥
30 years
=
5
Male
—
0.597
(0.201) **
Age 0.163 (0.076)
–
0.68
(0.237)
**
1.129
Student type Nominal Domestic student
=
1;
International Student
=
0 (ref.)
–
—
0.183
(0.096)
1.058
Student status Nominal Undergraduate
=
1; Post-
graduate
=
0 (ref.)
Domestic student
–
—
0.448
(0.246) .
–
1.085
Field of study Nominal There are two dummy variables:
Undergraduate 0.422 (0.221)
– –
1.250
Applied science and Social
science.
Applied science
– –
0.474
(0.253)
1.188
Health science is used as the
reference category.
Days of travel
per week
– –
—
0.25
(0.153)
1.030
Monthly income Ordinal
≤
$500
=
1; $501
–
$1000
=
2;
and
>
$1000
=
3
Car as a driver
– –
0.754
(0.257)
1.041
Distance from home to the
campus
Days of travel to the campus per
Continuous Kilometer
Ordinal 1 day
=
1; 2 days
=
2; 3 days
=
3;
Attitude 1
(Security
concerns)
—
0.33 (0.111)
**
**
–
1.17
(0.082)
1.078
week
Current travel mode choice to
the campus
Walk Cycle
&
Two-
wheeler Car driver Car
passenger Public transport
4 days
=
4; 5 days
=
5
There are four dummy variables:
Walk, Cycle
&
Two-wheeler, Car
driver, and Car passenger.
Public transport is used as the
reference category.
Latent variable Attitude 1
Coefficient
Item 4 0.374 (0.067)
***
Item 10 0.507 (0.075)
***
***
Knowledge of AV technology Ordinal Weak
=
1; Medium
=
2;
Item 7 0.376 (0.069)
AV type that interests
respondents
Nominal
Strong
=
3
Private car
=
1; Non-private
Intercept
***
AV choice in the case of no
budget barrier
ref.: reference.
Nominal
car
=
2
No
=
1; Yes
=
2
Item 4 4.319 (0.3)
***
Item 10 2.756 (0.364)
***
Item 7 3.496 (0.282)
university’s campus had no statistically significant effects on the three
output variables at the 0.1 significance level. A similar finding was re-
ported in Texas, the United States, that income was not associated with
interest in SAVs (Zmud et al., 2016). Unlike the findings of a Vietnamese
study, monthly income has a statistically positive effect on AV adoption
at the 0.05 significance level (Yuen et al., 2020a). Men had less
Covariances
AV type and AV
choice
AV type and AV
knowledge
AV choice and
AV knowledge
***
—
0.17 (0.109)
–
–
*
Nominal
Variable
Variable
Description
Parameter
Knowledge of
Interesting
AV
Collinearity
type
estimates
AV
AV type
choice
(VIF)

A. Soltani et al.
Case Studies on Transport Policy 9 (2021) 1956–1964
1962
knowledge of AV technology than women, but they were more likely to
adopt AVs if there was no financial barrier. The same finding was
discovered in the case study of Danang, Vietnam (Yuen et al., 2020b).
AVs were viewed favourably by men (Becker et al., 2018). Despite
having a better understanding of AV technology, older students had a
lower baseline preference for AV adoption. Unlike in Ireland and South
Korea, higher levels of educational attainment are positively associated
with pro-technology (Acheampong and Cugurullo, 2019), acceptance of
the benefits of AVs, and AV adoption (Yuen et al., 2020b). Non-private
AV was less appealing to domestic students than private AV. Under-
graduate students knew more about AV than postgraduate students,
which is surprising, and this factor is statistically significant at the 0.1
significant level. Students who study applied science are more likely to
choose AV than students who study other subjects.
The factor of days of travel was statistically negative. It implied that
students who travel often to the campus were less likely to choose AV.
The coefficient of car as a driver is positive, which implied that the
students who drove their cars to the campus were inclined to use AV.
However, this factor indicated no significant impact on prior knowledge
and AV technology and interesting AV type.
Attitude 1 had a statistically negative impact on knowledge of AV
technology but a positive effect on AV choice. As already mentioned,
Attitude 1 is a combination of items 4, 10, and 7. This suggests that
students who have a higher level of agreement with items 4 (moral and
ethical issues), 10 (vulnerable to remote control and hacking), and 7
(reduced parking cost) had lower knowledge of AV technology but a
higher preference for AV adoption. By contrast, a study conducted in the
Significance codes: ‘***
’
0.001; ‘**
’
0.01; ‘*
’
0.05; ‘.
’
0.1 ‘
–
’
defines no parameter estimate.
No. of iterations
=
28.
CFI = 0.91.
TLI = 0.97.
RMSEA = 0.05.
United States discovered that safety benefits and data privacy were the
important factors of AV adoption, with people who were more con-
cerned about data privacy issues being less likely to use AVs (Zmud et
al., 2016). Similarly, a European qualitative study using augmented
reality (AR) simulation technology discovered that when system de-
cisions are more difficult to predict, negative emotions associated with
loss of control in autonomous driving become more intense (Winter-
sberger et al., 2019).
The coefficients of items 4, 10, and 7 show that item 10 had the
greatest influence on AV choice. The intercept coefficients have no
interpretable meaning, and they are used to capture the average unob-
served effect. For the covariance among the three endogenous variables,
the covariance between AV choice and interesting AV type was almost
statistically significant at the 0.1 significance level, and the covariance
was negative. The covariance coefficient showed that students who were
interested in privately-owned AV by households were inclined to adopt
the AV technology. The covariance between interesting AV type and
knowledge of AV technology and the covariance between AV choice and
knowledge of AV technology were ignorable.
Because the variables are not correlated, the model confirms the
assumption of no multicollinearity. This is evident from the collinearity
index (VIF) values, as the VIF <10 is acceptable (Hair et al., 2009).
6.
Conclusions
Given the importance of AV adoption and diffusion, understanding the
factors that influence AV acceptance and adoption is critical. A solid
understanding of the determinants of AV adoption can help provide
insights into AV penetration. The current study looked at the effects of
socioeconomic characteristics, travel patterns, and attitudes on AV
technology knowledge, interesting AV type, and AV choice for tertiary
education trips. The study relied on a primary dataset of 165 observa-
tions gathered at UniSA’s three major campuses. The findings are
summarised below.
The results of the survey revealed a high level of concern about cyber-
security and AV malfunctions or glitches. Respondents shared their
thoughts on the various types of AV; however, AVs used for public
transportation were strongly prefered. When cost was not a consider-
ation, the majority of respondents expressed a desire to incorporate AV
technology into their livese. According to statistical analysis, students
who study applied sciences (engineering) are more likely than students
from other disciplines to adopt AV technology in the future. This can be
explained by the fact that students who acquire more knowledge about
technology topics are more willing to choose AVs for the future. The result
is in line with previous literature, for example, Chen and Chiachun (2019)
found in a survey of university students in Taiwan that students studying
pure sciences and engineering are significantly more positive than their
counterparts in the social sciences. and arts majors, towards the adoption
of AV and smart homes. Similarly, a study of a sample of students from
two universities in Romania found a significant difference in attitudes
towards artificial intelligence (AI) between students of technical
disciplines compared to those of humanities (Gherhes and Obrad, 2018).
Students who drove to university campuses had a higher baseline
preference to adopt AV technology, whereas those who travelled to
campuses frequently were less likely to adopt AV technology.
Socioeconomic factors also play a significant role in AV technology
acceptance and adoption. Gender and age groups were found to have a
significant influence on attitudes towards AV technology. Male students
were more likely than female students to accept the AVs for future use.
The younger generation was more likely to accept the AVs for future use.

A. Soltani et al.
Case Studies on Transport Policy 9 (2021) 1956–1964
1963
The inverse U-shape form can be used to investigate the effects of age
and income on possible nonlinear relationships (Tuu and Olsen, 2010).
Surprisingly, prior knowledge of self-driving vehicles had no effect on
attitudes towards AV technology.
The arrival of AVs, according to the literature, has the potential to
affect students’ travel patterns in a variety of ways due to the numerous
benefits and drawbacks of AVs. This study confirmed the importance of
students being aware of the burgeoning AVs technology. After observing
the series of benefits of AVs and the needs, the South Australian State
Government may facilitate and run trials of AVs in university settings,
focusing on students (young population of the public) to experience AVs
on public roads. FLEX, a driverless shuttle bus, has been in operation at
Flinders University since 2018 (Flinders University, 2018).
This study’s findings are consistent with previous Australian
research on the barriers to adopting AVs in closed environments, which
included privacy, security, reliability, and trust (Kaur and Rampersad,
2018). To address potential vulnerabilities, the Australian National
Cyber Security Strategy (Commonwealth of Australia, 2016) may
necessitate additional examinations and updates in order to empower
the AVs’s industry to protect itself and become more “cyber-resilient”
(Tam et al., 2021). The government should prepare AV services to meet
the needs and requirements of tertiary students in South Australia’s
urban and regional areas. In addition to the government’s obligation to
enact legislation to address safety, privacy and cyber-security concerns
(Taeihagh and Lim, 2019), transportation planners/engineers should
consider developing the necessary road infrastructure (e.g., traffic signs
and road markings) for safety concerns.
The COVID-19 lockdowns had not yet been implemented at the time
of this study, whereas during the pandemic era, commuting habits,
particularly via public transportation, had significantly changed, and
concerns about crowds and hygiene had grown throughout Australia
(Beck et al., 2021). The sample size of the study could be increased by
including samples from additional Adelaide-based higher education in-
stitutions. The inclusion of the influences of the campus’s surrounding
built environment on students’ commuting patterns (Nash and Mitra,
2019, Whalen et al., 2013) has the potential to increase the model’s
explanatory power. Furthermore, much of the previous research on at-
titudes towards AVs was quantitative and based on the hypothetico-
deductive approach (Acheampong and Cugurullo, 2019, Haboucha et
al., 2017). In future studies, qualitative approaches such as in-depth and
structured interviews (Pettigrew et al., 2018), may be chosen to
generate small-group discussions with informant bodies such as poli-
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